4 Interface agents
This chapter covers
- Examining the key components that make up an interface agent
- Understanding Large Action Models and how they generate action sequences
- Investigating strategies for representing interfaces to AI models effectively
- Implementing an interface agent from scratch using Playwright
- Discussing challenges faced by interface agents, including latency, representation, and reliability issues
4.1 When Code and APIs Aren’t Enough: The Role of Interface Agents
In Chapter 2, we explored how to build your first multi-agent application using AutoGen, starting from defining agent workflows, given the agents access to generative ai models and tools (code interpreter, functions) and enabling them to interact to solve tasks. We outlined how the quality of tools that agents have access to can significantly impact the tasks they can solve, and outlined how general purpose tools such as code interpreters or the ability to directly control or drive applications can be used to solve a wide range of tasks.
Importantly, though many tasks can be accomplished through code execution (for example, the LLM generates code to solve the task, or can correctly select an existing function to solve the task), there are task scenarios where a code execution approach falls short (as illustrated in figure 4.1 ).